Video analysis with convolutional attention recurrent neural networks

ABSTRACT

A method of processing data within a convolutional attention recurrent neural network (RNN) includes generating a current multi-dimensional attention map. The current multi-dimensional attention map indicates areas of interest in a first frame from a sequence of spatio-temporal data. The method further includes receiving a multi-dimensional feature map. The method also includes convolving the current multi-dimensional attention map and the multi-dimensional feature map to obtain a multi-dimensional hidden state and a next multi-dimensional attention map. The method identifies a class of interest in the first frame based on the multi-dimensional hidden state and training data.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. Provisional PatentApplication No. 62/307,366, filed on Mar. 11, 2016, and titled “VIDEOANALYSIS WITH CONVOLUTIONAL ATTENTION RECURRENT NEURAL NETWORKS,” thedisclosure of which is expressly incorporated by reference herein in itsentirety.

BACKGROUND

Field

Certain aspects of the present disclosure generally relate to machinelearning and, more particularly, to improving systems and methods ofconsidering the spatial relationship of an input when processing amulti-dimension hidden state and a multi-dimension attention map.

Background

An artificial neural network, such as an artificial neural network withan interconnected group of artificial neurons (e.g., neuron models), maybe a computational device or may be a method to be performed by acomputational device.

A convolutional neural network (CNN) refers to a type of feed-forwardartificial neural network. Convolutional neural networks may includecollections of neurons, each neuron having a receptive field and alsocollectively tiling an input space. Convolutional neural networks may beused for pattern recognition and/or input classification.

Recurrent neural networks (RNNs) refer to a class of neural network,which includes a cyclical connection between nodes or units of thenetwork. The cyclical connection creates an internal state that mayserve as a memory that enables recurrent neural networks to modeldynamical systems. That is, cyclical connections offer recurrent neuralnetworks the ability to encode memory. Thus, if successfully trained,recurrent neural networks may be specified for sequence learningapplications.

A recurrent neural network may be used to implement a long short-termmemory (LSTM). For example, the long short-term memory may beimplemented in a microcircuit including multiple units to store valuesin memory using gating functions and multipliers. A long short-termmemory may hold a value in memory for an arbitrary length of time. Assuch, long short-term memory may be useful for learning, classificationsystems (e.g., handwriting and speech recognition systems), and/or otherapplications.

In conventional systems, a recurrent network, such as a recurrent neuralnetwork, is used to model sequential data. Recurrent neural networks mayhandle vanishing gradients. Thus, recurrent neural networks may improvethe modeling of data sequences. Consequently, recurrent neural networksmay improve the modelling of the temporal structure of sequential data,such as videos.

Still, in conventional recurrent neural networks (e.g., standard RNNs),input dimensions are treated equally, as all dimensions equallycontribute to the internal state of the recurrent neural network unit.For sequential temporal data, such as videos, some dimensions are moreimportant than others. An important area may refer to an area withaction, an object, or an event. Moreover, at different times, differentdimensions may be more important than other dimensions. For example, ina video with action, the locations with the action may be specified tohave a greater weight and an increased contribution to the internalstate of the recurrent neural network in comparison to locations withoutaction. Therefore, conventional systems have proposed an attentionrecurrent neural network model that predicts an attention saliencyvector for the input that weighs different dimensions according to theirimportance. Although an attention recurrent neural network weighsdifferent dimensions according to their importance, there is also a needto consider the spatial dimensions of sequential data.

SUMMARY

In one aspect of the present disclosure, a method of processing datawithin a convolutional attention recurrent neural network is disclosed.The method includes generating a current multi-dimensional attentionmap. The current multi-dimensional attention map indicates areas ofinterest in a first frame from a sequence of spatio-temporal data. Themethod further includes receiving a multi-dimensional feature map. Themethod also includes convolving the current multi-dimensional attentionmap and the multi-dimensional feature map to obtain a multi-dimensionalhidden state and a next multi-dimensional attention map. The methodstill further includes identifying a class of interest in the firstframe based on the multi-dimensional hidden state and training data.

Another aspect of the present disclosure is directed to an apparatusincluding means for generating a current multi-dimensional attentionmap. The current multi-dimensional attention map indicates areas ofinterest in a first frame from a sequence of spatio-temporal data. Theapparatus also includes means for receiving a multi-dimensional featuremap. The apparatus further includes means for convolving the currentmulti-dimensional attention map and the multi-dimensional feature map toobtain a multi-dimensional hidden state and a next multi-dimensionalattention map. The apparatus still further includes means foridentifying a class of interest in the first frame based on themulti-dimensional hidden state and training data.

In another aspect of the present disclosure, a non-transitorycomputer-readable medium with non-transitory program code recordedthereon is disclosed. The program code for processing data within aconvolutional attention recurrent neural network is executed by aprocessor and includes program code to generate a currentmulti-dimensional attention map. The current multi-dimensional attentionmap indicates areas of interest in a first frame from a sequence ofspatio-temporal data. The program code also includes program code toreceive a multi-dimensional feature map. The program code furtherincludes program code to convolve the current multi-dimensionalattention map and the multi-dimensional feature map to obtain amulti-dimensional hidden state and a next multi-dimensional attentionmap. The program code still further includes program code to identify aclass of interest in the first frame based on the multi-dimensionalhidden state and training data.

Another aspect of the present disclosure is directed to an apparatus forprocessing data within a convolutional attention recurrent neuralnetwork, the apparatus having a memory unit and one or more processorscoupled to the memory unit. The processor(s) is configured to generate acurrent multi-dimensional attention map. The current multi-dimensionalattention map indicates areas of interest in a first frame from asequence of spatio-temporal data. The processor(s) is also configured toreceive a multi-dimensional feature map. The processor(s) is furtherconfigured to convolve the current multi-dimensional attention map andthe multi-dimensional feature map to obtain a multi-dimensional hiddenstate and a next multi-dimensional attention map. The processor(s) isfurther configured to identify a class of interest in the first framebased on the multi-dimensional hidden state and training data.

Additional features and advantages of the disclosure will be describedbelow. It should be appreciated by those skilled in the art that thisdisclosure may be readily utilized as a basis for modifying or designingother structures for carrying out the same purposes of the presentdisclosure. It should also be realized by those skilled in the art thatsuch equivalent constructions do not depart from the teachings of thedisclosure as set forth in the appended claims. The novel features,which are believed to be characteristic of the disclosure, both as toits organization and method of operation, together with further objectsand advantages, will be better understood from the following descriptionwhen considered in connection with the accompanying figures. It is to beexpressly understood, however, that each of the figures is provided forthe purpose of illustration and description only and is not intended asa definition of the limits of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify correspondingly throughout.

FIG. 1 illustrates an example implementation of designing a neuralnetwork using a system-on-a-chip (SOC), including a general-purposeprocessor in accordance with certain aspects of the present disclosure.

FIG. 2 illustrates an example implementation of a system in accordancewith aspects of the present disclosure.

FIG. 3A is a diagram illustrating a neural network in accordance withaspects of the present disclosure.

FIG. 3B is a block diagram illustrating an exemplary deep convolutionalnetwork (DCN) in accordance with aspects of the present disclosure.

FIG. 4 is a schematic diagram illustrating a recurrent neural network(RNN) in accordance with aspects of the present disclosure.

FIG. 5A is a diagram illustrating an image in a video frame for which alabel is to be predicted in accordance with aspects of the presentdisclosure.

FIG. 5B is a diagram illustrating an exemplary architecture of anattention recurrent neural network (RNN) network for predicting anaction in a video frame in accordance with aspects of the presentdisclosure.

FIG. 6 is a diagram illustrating an exemplary architecture forpredicting action in a video frame in accordance with aspects of thepresent disclosure.

FIGS. 7A and 7B illustrate examples of conventional networks forprocessing data.

FIG. 7C illustrates an example of a convolutional attention recurrentneural network (RNN) according to an aspect of the present disclosure.

FIG. 8 illustrates a flow diagram for a method of processing within aconvolutional attention recurrent neural network (RNN) according toaspects of the present disclosure.

DETAILED DESCRIPTION

The detailed description set forth below, in connection with theappended drawings, is intended as a description of variousconfigurations and is not intended to represent the only configurationsin which the concepts described herein may be practiced. The detaileddescription includes specific details for the purpose of providing athorough understanding of the various concepts. However, it will beapparent to those skilled in the art that these concepts may bepracticed without these specific details. In some instances, well-knownstructures and components are shown in block diagram form in order toavoid obscuring such concepts.

Based on the teachings, one skilled in the art should appreciate thatthe scope of the disclosure is intended to cover any aspect of thedisclosure, whether implemented independently of or combined with anyother aspect of the disclosure. For example, an apparatus may beimplemented or a method may be practiced using any number of the aspectsset forth. In addition, the scope of the disclosure is intended to coversuch an apparatus or method practiced using other structure,functionality, or structure and functionality in addition to or otherthan the various aspects of the disclosure set forth. It should beunderstood that any aspect of the disclosure disclosed may be embodiedby one or more elements of a claim.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

Although particular aspects are described herein, many variations andpermutations of these aspects fall within the scope of the disclosure.Although some benefits and advantages of the preferred aspects arementioned, the scope of the disclosure is not intended to be limited toparticular benefits, uses or objectives. Rather, aspects of thedisclosure are intended to be broadly applicable to differenttechnologies, system configurations, networks and protocols, some ofwhich are illustrated by way of example in the figures and in thefollowing description of the preferred aspects. The detailed descriptionand drawings are merely illustrative of the disclosure rather thanlimiting, the scope of the disclosure being defined by the appendedclaims and equivalents thereof.

Motion information has traditionally been an important ingredient inautomated video understanding, both for traditional video encodingsusing dense trajectories or more recent two-stream deep convolutionalneural network architectures. Unlike convolutional neural networksintended for images, recurrent neural networks were originally proposedto model sequential data. A variant of a recurrent neural network is thelong short-term memory (LSTM) architecture. The long short-term memorynetwork can handle vanishing gradients. Therefore, in comparison toconventional techniques, the recurrent neural network may improve themodeling of sequences of temporal data. Consequently, recurrent neuralnetworks may improve the modelling for sequential temporal structure ofvideos to determine the location of an action, an object, and/or anevent in the video.

As previously discussed, conventional recurrent neural networks treatall locations of a frame equally and do not discriminate between thevarious spatial locations in the frame. Still, to improve theclassification of an object, an event, or an action of spatio-temporaldata, the recurrent neural network should consider that certain regionsare more pertinent than other regions of a frame. Some conventionalapproaches have proposed an attention recurrent neural network foraction classification. The attention recurrent neural network assigns agreater importance (e.g., attention) to particular frame locations withactions, events, and/or objects of interest. The attention may beimplemented with a saliency map (e.g., a map of conspicuous regions),which provides the recurrent neural network with an area of focus for aframe.

Conventional attention recurrent neural network use the recurrent neuralnetwork state at frame x to generate the attention for frame x+1, suchthat the attention recurrent neural network may predict the location ofthe action in the next frame. However, conventional attention recurrentneural networks rely on appearance only and ignore the motion patternsof sequential spatio-temporal data, such as videos. In most cases, aframe (e.g., image) from sequential spatio-temporal data, such as avideo, has spatial dimensions. That is, there is a spatial correlationbetween pixels of the frame.

Thus, aspects of the present disclosure are directed to a convolutionalrecurrent neural network specified to learn spatio-temporal dynamics ofsequential spatio-temporal data. That is, the convolutional attentionrecurrent neural network of the present disclosure may accommodatecharacteristics of the multi-dimensional structure of a video frame.Specifically, the convolutional attention recurrent neural network maytreat the spatial dimensions of its input differently by varying theweights corresponding to different spatial dimensions. Specifically,aspects of the present disclosure are directed to an attention recurrentneural network, such as a convolutional attention recurrent neuralnetwork, which uses the spatial characteristics of an input foranalytics.

FIG. 1 illustrates an example implementation of the aforementioned videoanalysis using a system-on-a-chip (SOC) 100, which may include ageneral-purpose processor (CPU) or multi-core general-purpose processors(CPUs) 102 in accordance with certain aspects of the present disclosure.Variables (e.g., neural signals and synaptic weights), system parametersassociated with a computational device (e.g., neural network withweights), delays, frequency bin information, and task information may bestored in a memory block associated with a neural processing unit (NPU)108, in a memory block associated with a CPU 102, in a memory blockassociated with a graphics processing unit (GPU) 104, in a memory blockassociated with a digital signal processor (DSP) 106, in a dedicatedmemory block 118, or may be distributed across multiple blocks.Instructions executed at the general-purpose processor 102 may be loadedfrom a program memory associated with the CPU 102 or may be loaded froma dedicated memory block 118.

The SOC 100 may also include additional processing blocks tailored tospecific functions, such as a GPU 104, a DSP 106, a connectivity block110, which may include fourth generation long term evolution (4G LTE)connectivity, unlicensed Wi-Fi connectivity, USB connectivity, Bluetoothconnectivity, and the like, and a multimedia processor 112 that may, forexample, detect and recognize gestures. In one implementation, the NPUis implemented in the CPU, DSP, and/or GPU. The SOC 100 may also includea sensor processor 114, image signal processors (ISPs) 116, and/ornavigation 120, which may include a global positioning system.

The SOC 100 may be based on an ARM instruction set. In an aspect of thepresent disclosure, the instructions loaded into the general-purposeprocessor 102 may comprise code to generate a current multi-dimensionalattention map. The instructions loaded into the general-purposeprocessor 102 may also comprise code to receive a multi-dimensionalfeature map. The instructions loaded into the general-purpose processor102 may further comprise code to convolve the current multi-dimensionalattention map and the multi-dimensional feature map to obtain amulti-dimensional hidden state and a next multi-dimensional attentionmap. The instructions loaded into the general-purpose processor 102 mayfurther comprise code to identify a class of interest in the first framebased on the multi-dimensional hidden state and training data.

FIG. 2 illustrates an example implementation of a system 200 inaccordance with certain aspects of the present disclosure. Asillustrated in FIG. 2, the system 200 may have multiple local processingunits 202 that may perform various operations of methods describedherein. Each local processing unit 202 may comprise a local state memory204 and a local parameter memory 206 that may store parameters of aneural network. In addition, the local processing unit 202 may have alocal (neuron) model program (LMP) memory 208 for storing a local modelprogram, a local learning program (LLP) memory 210 for storing a locallearning program, and a local connection memory 212. Furthermore, asillustrated in FIG. 2, each local processing unit 202 may interface witha configuration processor unit 214 for providing configurations forlocal memories of the local processing unit, and with a routingconnection processing unit 216 that provides routing between the localprocessing units 202.

In one configuration, a processing model is configured for generating acurrent multi-dimensional attention map; receiving a multi-dimensionalfeature map; convolving the current multi-dimensional attention map andthe multi-dimensional feature map to obtain a multi-dimensional hiddenstate and a next multi-dimensional attention map; and identifying aclass of interest in the first frame based on the multi-dimensionalhidden state and training data. The model includes generating means,receiving means, convolving means, and/or identifying means. In oneconfiguration, the generating means, receiving means, convolving means,and/or identifying means may be the general-purpose processor 102,program memory associated with the general-purpose processor 102, memoryblock 118, local processing units 202, and or the routing connectionprocessing units 216 configured to perform the functions recited. Inanother configuration, the aforementioned means may be any module or anyapparatus configured to perform the functions recited by theaforementioned means.

Neural networks may be designed with a variety of connectivity patterns.In feed-forward networks, information is passed from lower to higherlayers, with each neuron in a given layer communicating to neurons inhigher layers. A hierarchical representation may be built up insuccessive layers of a feed-forward network, as described above. Neuralnetworks may also have recurrent or feedback (also called top-down)connections. In a recurrent connection, the output from a neuron in agiven layer may be communicated to another neuron in the same layer. Arecurrent architecture may be helpful in recognizing patterns that spanmore than one of the input data chunks that are delivered to the neuralnetwork in a sequence. A connection from a neuron in a given layer to aneuron in a lower layer is called a feedback (or top-down) connection. Anetwork with many feedback connections may be helpful when therecognition of a high-level concept may aid in discriminating theparticular low-level features of an input.

Referring to FIG. 3A, the connections between layers of a neural networkmay be fully connected 302 or locally connected 304. In a fullyconnected network 302, a neuron in a first layer may communicate itsoutput to every neuron in a second layer, so that each neuron in thesecond layer will receive input from every neuron in the first layer.Alternatively, in a locally connected network 304, a neuron in a firstlayer may be connected to a limited number of neurons in the secondlayer. A convolutional network 306 may be locally connected, and isfurther configured such that the connection strengths associated withthe inputs for each neuron in the second layer are shared (e.g., 308).More generally, a locally connected layer of a network may be configuredso that each neuron in a layer will have the same or a similarconnectivity pattern, but with connections strengths that may havedifferent values (e.g., 310, 312, 314, and 316). The locally connectedconnectivity pattern may give rise to spatially distinct receptivefields in a higher layer, because the higher layer neurons in a givenregion may receive inputs that are tuned through training to theproperties of a restricted portion of the total input to the network.

Locally connected neural networks may be well suited to problems inwhich the spatial location of inputs is meaningful. For instance, anetwork 300 designed to recognize visual features from a car-mountedcamera may develop high layer neurons with different propertiesdepending on their association with the lower versus the upper portionof the image. Neurons associated with the lower portion of the image maylearn to recognize lane markings, for example, while neurons associatedwith the upper portion of the image may learn to recognize trafficlights, traffic signs, and the like.

A DCN may be trained with supervised learning. During training, a DCNmay be presented with an image, such as a cropped image of a speed limitsign 326, and a “forward pass” may then be computed to produce an output322. The output 322 may be a vector of values corresponding to featuressuch as “sign,” “60,” and “100.” The network designer may want the DCNto output a high score for some of the neurons in the output featurevector, for example the ones corresponding to “sign” and “60” as shownin the output 322 for a network 300 that has been trained. Beforetraining, the output produced by the DCN is likely to be incorrect, andso an error may be calculated between the actual output and the targetoutput. The weights of the DCN may then be adjusted so that the outputscores of the DCN are more closely aligned with the target.

To adjust the weights, a learning algorithm may compute a gradientvector for the weights. The gradient may indicate an amount that anerror would increase or decrease if the weight were adjusted slightly.At the top layer, the gradient may correspond directly to the value of aweight connecting an activated neuron in the penultimate layer and aneuron in the output layer. In lower layers, the gradient may depend onthe value of the weights and on the computed error gradients of thehigher layers. The weights may then be adjusted so as to reduce theerror. This manner of adjusting the weights may be referred to as “backpropagation” as it involves a “backward pass” through the neuralnetwork.

In practice, the error gradient of weights may be calculated over asmall number of examples, so that the calculated gradient approximatesthe true error gradient. This approximation method may be referred to asstochastic gradient descent. Stochastic gradient descent may be repeateduntil the achievable error rate of the entire system has stoppeddecreasing or until the error rate has reached a target level.

After learning, the DCN may be presented with new images 326 and aforward pass through the network may yield an output 322 that may beconsidered an inference or a prediction of the DCN.

Deep convolutional networks (DCNs) are networks of convolutionalnetworks, configured with additional pooling and normalization layers.DCNs have achieved state-of-the-art performance on many tasks. DCNs canbe trained using supervised learning in which both the input and outputtargets are known for many exemplars and are used to modify the weightsof the network by use of gradient descent methods.

DCNs may be feed-forward networks. In addition, as described above, theconnections from a neuron in a first layer of a DCN to a group ofneurons in the next higher layer are shared across the neurons in thefirst layer. The feed-forward and shared connections of DCNs may beexploited for fast processing. The computational burden of a DCN may bemuch less, for example, than that of a similarly sized neural networkthat comprises recurrent or feedback connections.

The processing of each layer of a convolutional network may beconsidered a spatially invariant template or basis projection. If theinput is first decomposed into multiple channels, such as the red,green, and blue channels of a color image, then the convolutionalnetwork trained on that input may be considered three-dimensional, withtwo spatial dimensions along the axes of the image and a third dimensioncapturing color information. The outputs of the convolutionalconnections may be considered to form a feature map in the subsequentlayer 318 and 320, with each element of the feature map (e.g., 320)receiving input from a range of neurons in the previous layer (e.g.,318) and from each of the multiple channels. The values in the featuremap may be further processed with a non-linearity, such as arectification, max(0,x). Values from adjacent neurons may be furtherpooled, which corresponds to down sampling, and may provide additionallocal invariance and dimensionality reduction. Normalization, whichcorresponds to whitening, may also be applied through lateral inhibitionbetween neurons in the feature map.

FIG. 3B is a block diagram illustrating an exemplary deep convolutionalnetwork 350. The deep convolutional network 350 may include multipledifferent types of layers based on connectivity and weight sharing. Asshown in FIG. 3B, the exemplary deep convolutional network 350 includesmultiple convolution blocks (e.g., C1 and C2). Each of the convolutionblocks may be configured with a convolution layer, a normalization layer(LNorm), and a pooling layer. The convolution layers may include one ormore convolutional filters, which may be applied to the input data togenerate a feature map. Although only two convolution blocks are shown,the present disclosure is not so limiting, and instead, any number ofconvolutional blocks may be included in the deep convolutional network350 according to design preference. The normalization layer may be usedto normalize the output of the convolution filters. For example, thenormalization layer may provide whitening or lateral inhibition. Thepooling layer may provide down sampling aggregation over space for localinvariance and dimensionality reduction.

The parallel filter banks, for example, of a deep convolutional networkmay be loaded on a CPU 102 or GPU 104 of an SOC 100, optionally based onan ARM instruction set, to achieve high performance and low powerconsumption. In alternative embodiments, the parallel filter banks maybe loaded on the DSP 106 or an ISP 116 of an SOC 100. In addition, theDCN may access other processing blocks that may be present on the SOC,such as processing blocks dedicated to sensors 114 and navigation 120.

The deep convolutional network 350 may also include one or more fullyconnected layers (e.g., FC1 and FC2). The deep convolutional network 350may further include a logistic regression (LR) layer. Between each layerof the deep convolutional network 350 are weights (not shown) that areto be updated. The output of each layer may serve as an input of asucceeding layer in the deep convolutional network 350 to learnhierarchical feature representations from input data (e.g., images,audio, video, sensor data and/or other input data) supplied at the firstconvolution block C1.

FIG. 4 is a schematic diagram illustrating a recurrent neural network(RNN) 400. The recurrent neural network 400 includes an input layer 402,a hidden layer 404 with recurrent connections, and an output layer 406.Given an input sequence X with multiple input vectors x_(T) (e.g.,X={x₀, x₁, x₂ . . . x_(T)}), the recurrent neural network 400 willpredict a classification label y_(t) for each output vector z_(T) of anoutput sequence Z (e.g., Z={z₀ . . . z_(T)}). For FIG. 4, x_(t)ε

^(N), y_(t)ε

^(C), and z_(t)ε

^(C) As shown in FIG. 4, a hidden layer 404 with M units (e.g., h_(o) .. . h_(t)) is specified between the input layer 402 and the output layer406. The M units of the hidden layer 404 store information on theprevious values ({acute over (t)}<t) of the input sequence X. The Munits may be computational nodes (e.g., neurons). In one configuration,the recurrent neural network 400 receives an input x_(T) and generates aclassification label y_(t) of the output z_(T) by iterating theequations:

s _(t) =W _(hx) _(x) _(t) +W _(hh) h _(t−1) +b _(h)  (1)

h _(t)=ƒ(s _(t))  (2)

o _(t) =W _(yh) h _(t) +b _(y)  (3)

y _(t) =g(o _(t))  (4)

where W_(hx), W_(hh), and W_(yh) are the weight matrices, b_(h) andb_(y) are the biases, s_(t)ε

^(M) and o_(t)ε

^(C) are inputs to the hidden layer 404 and the output layer 406,respectively, and ƒ and g are nonlinear functions. The function ƒ maycomprise a rectifier linear unit (RELU) and, in some aspects, thefunction g may comprise a linear function or a softmax function. Inaddition, the hidden layer nodes are initialized to a fixed bias bi suchthat at t=0 h_(o)=bi. In some aspects, bi may be set to zero (e.g.,bi=0). The objective function, C(θ), for a recurrent neural network witha single training pair (x,y) is defined as C(θ)=Σ_(t)L_(t)(z, y(θ)),where θ represents the set of parameters (weights and biases) in therecurrent neural network. For regression problems,L_(t)=∥(z_(t)−y_(t))²∥ and for multi-class classification problems,L_(t)=−Σ_(j)z_(tj) log(y_(tj)).

FIG. 5A is a diagram illustrating a frame (e.g., image) from a sequenceof frames (e.g., a video) for which a classification label is to bepredicted. Referring to FIG. 5A, the frame (I_(t)) 504 is provided as aninput. An attention map (a_(t)) 502 corresponding to the frame 504 maybe predicted, for example by a multi-layer perceptron using the previoushidden state (h_(t−1)) and the current feature map (X_(t)) as input. Theattention map 502 may be combined with a feature map (X_(t)) generatedfrom the frame 504. The feature map may be generated from an upperconvolution layer of a convolutional neural network (e.g., FC2 in FIG.3B) or via a recurrent neural network, for example. The feature map maybe a two-dimensional (2D) or three-dimensional (3D) feature map, forappearance, optical flow, motion boundaries (e.g., gradient of opticalflow), semantic segmentation at a feature level, and/or the like.

The attention map 502 provides a recurrent neural network, or a longshort-term memory network, with the location of action in a frame 504.The action may be determined by using appearance motion information. Asshown in FIG. 5A, an attention map a_(t) and a feature map X_(t) may becombined by a weighted sum over all the spatial locations in the frameto compute a weighted feature map x_(t) (e.g., x_(t)=Σ_(k) a_(t)^(k)X_(t) ^(k)) as an input, where X_(t) ^(k) indicates a feature vector(slice) in the feature map X_(t) at each location k, and a_(t) ^(k) isthe weight in the attention map at its location.

FIG. 5B illustrates an example of an architecture 500 of an attentionrecurrent neural network for predicting an action in a frame. Theexemplary architecture 500 may comprise a recurrent neural network, suchas a long short-term memory (e.g., an attention long short-term memory(LSTM) network). The exemplary architecture 500 may include an inputlayer with input units (e.g., x₁, x₂, x₃, . . . ), a hidden layer havinghidden units (e.g., r₁, r₂, r₃, . . . ) and an output layer with outputunits (e.g., y₁, y₂, y₃, . . . ) and attention maps (e.g., a1, a2, a3, .. . ). Each of the units may, for example, comprise artificial neuronsor neural units. As shown in FIG. 5B, for a first frame, an attentionmap a₁ and a feature map X₁ are supplied and used to compute a firstinput x₁ to a first hidden layer r₁ of the attention recurrent neuralnetwork. In some aspects, the feature map of the first frame maycomprise appearance (e.g., visual information form the content of theframe).

The first hidden unit r₁ predicts a first classification label y₁ andoutputs a first hidden state h₁ that is used to generate secondattention map a₂ (e.g., subsequent attention map) for a second frame.The classification label may be referred to as a label, a class ofinterest, or an action label. Furthermore, the classification labelindicates an action, an object, an event in the frame and/or thesequence of frames. The first hidden unit r₁ also outputs the firsthidden state h₁ to a second hidden unit r₂. The hidden state at a timestep is an internal representation of the neural network at that timestep.

The second attention map a₂ and the feature map X₂ may be used todetermine a second input x₂, which is input to the second hidden unit r₂(that generates a second hidden state h₂). The hidden state of thesecond hidden unit r₂ is then used to predict a third attention map a₃and a second classification label y₂ (e.g., the action) for the secondframe.

The feature map X₃ and attention map a₃ may be used to determine inputx₃, which is supplied to the third hidden unit r₃. The hidden state ofthe third hidden unit r₃ is used to predict the next attention map andan action label y₃ (e.g., the action of frame 3) for a third frame. Thisprocess may then be repeated for each subsequent frame at eachsucceeding time step.

FIG. 6 is a diagram illustrating an exemplary architecture 600 forpredicting action in a frame and/or a sequence of frames, in accordancewith aspects of the present disclosure. The exemplary architecture 600may be configured as a stratified network including upper layerrecurrent neural networks and lower layer recurrent neural networks(e.g., two layers of recurrent neural networks). Although the exemplaryarchitecture includes recurrent neural networks, this is exemplary, asother neural network architectures are also considered. For example, theupper layer may be a recurrent neural network and the lower layer may bean upper convolution layer of a convolutional neural network. Forexample, the upper layer or lower layer may comprise a recurrent neuralnetwork and the upper layer or lower layer may comprise multiple stackedrecurrent neural network or long short-term memory layers.

The lower layer recurrent neural network uses the motion informationf_(t) and the hidden states from the previous frame to generate anattention saliency map for the current frame t. The motion informationf_(t) may be produced from optical flow, which may be estimated usingthe current frame and the next frame. The motion information f_(t) maybe produced via an upper convolution layer of a CNN. For example, thelower layer recurrent neural network unit r_(l2) may use the hiddenstate of the previous hidden unit r_(l1) the hidden state h_(p1) of theupper layer recurrent neural network unit r_(p1) along with motioninformation f₂ to generate the attention map a₂ for a second frame. Assuch, the lower layer recurrent neural network may provide the upperlayer recurrent neural network with attention maps. The layer units maybe artificial neurons or neural units.

The generated attention map a_(t) for a current frame may be combinedwith a representation of the current frame of a sequence of frames(e.g., video stream). The frame representation may be a frame featuremap X_(t) to create the input x_(t) for the upper layer recurrent neuralnetworks. In turn, the upper layer recurrent neural network may beconfigured to output a classification label y_(t) for the current framet. In addition, the upper layer recurrent neural network r_(pt) mayoutput a hidden state h_(t) of the upper layer recurrent neural networkunit, which may be supplied to a subsequent hidden units r_(It) of thelower layer recurrent neural network and used to calculate or infer theattention map for the subsequent frame a_(t+1).

In operation, as shown in FIG. 6, for a first frame (time step t₁), anattention map a₁ may be predicted using a first lower hidden unitr_(I1), which receives the motion information f_(t) as input. The motioninformation may be produced from an optical flow, which may be computedusing the first frame and the second frame. In one configuration, theoptical flow is computed using two adjacent frames. The attention map a₁is applied to the frame feature map X₁ to calculate an input x₁comprising combined features. The combined features of the first inputx₁ are output to the first upper hidden layer r_(p1), which may predicta first classification label y₁. For example, the first classificationlabel may label the frame as containing a diver, as shown in the frame504 of FIG. 5A.

In addition, the first upper layer unit r_(p1) outputs its hidden stateh_(p1) as an input to the subsequent lower hidden unit r_(l2) at a nextframe. In this example, the second lower hidden unit r_(I2) receives alower unit hidden state h_(I1) from the first lower hidden unit r_(I1).The hidden state of the first upper hidden unit r_(p1) is also providedto the second upper hidden unit r_(p2) of the upper layer LSTM. Thehidden unit r_(l2) also receives an motion input f₂ produced fromoptical flow. The optical flow input may be a field of vectors thatpredict how pixels at frame (e.g., frame t) will move to pixels at anext frame (e.g., frame t+1). For example, if the optical flow vector isa 2D vector expressed as 2,3 at x,y, the optical flow indicates that thepixels of frame t will move 2 pixels right and 3 pixels up in thesubsequent frame t+1. That is, the optical flow tracks action in thepixels and considers motion to predict the most salient features withinthe video.

Using the optical flow and the hidden states (e.g., h_(p1) and h_(I1))from previous hidden units r_(p1) and r_(l1) a new attention map a₂ maybe inferred via the hidden layer unit r_(l2). The second attention mapa₂ may be used with the frame appearance feature map of the second frameto calculate a second input x₂ to the second upper hidden unit r_(p2).The second attention map a₂, which may include motion information (e.g.,optical flow), may improve the identification of the regions of interestin the frame (e.g., actors) in comparison to previous frames. Thus, thesecond attention map may improve the label prediction.

The second upper hidden unit r_(p2) may then predict a new hidden stateh_(p2) that infers a second classification label y₂. The hidden state ofthe second upper hidden unit r_(p2) may be output to the third lowerhidden unit r_(I3) and may be used along with the hidden state of thesecond lower hidden unit r_(I2) as well as the motion input (e.g.,optical flow) f₃ to compute a third attention map a₃ for a third frame.The third attention map a₃ may be used along with the featurerepresentation of the third frame (e.g., third frame appearance map X₃)to compute a third input x₃ for the third upper hidden unit r_(p3),which in turn predicts a new hidden state that infers a classificationlabel for the third frame. Thereafter, the process may be repeated.

Aspects of the present disclosure are not limited to the number ofexemplary hidden units of a neural network shown in FIGS. 5B and 6. Ofcourse, more or less hidden units of a neural network are alsocontemplated.

The attention map instructs a recurrent neural network of the locationin a frame that an action takes place using appearance motioninformation, such as optical flow. In some aspects, the optical flow maybe described via a field of vectors that indicate how a pixel will movefrom one frame to the next. The optical flow tracks action in the videopixels and considers motion to predict salient features (e.g., importantspatial locations) within the video.

Sequential Data Analysis with Convolutional Attention Recurrent NeuralNetworks

As previously discussed, aspects of the present disclosure are directedto a convolutional attention recurrent neural network that uses spatialcharacteristics of sequential data. The sequential data may be referredto as a sequence of spatio-temporal data or a video. Although describedgenerally with respect to recurrent neural networks, the presentdisclosure can employ a particular type of recurrent neural network,such as a long short-term memory (LSTM) network.

Conventional recurrent neural networks assume a one-dimensional inputvector. Thus, the input-to-state and state-to-state operations withinthe recurrent neural network are implemented as inner products. Aspectsof the present disclosure omit the gate formulations of a recurrentneural network, such as an LSTM network, to simplify the equation. Inone configuration, the recurrent connection is defined as:

$\begin{matrix}{h_{t} = {\sigma \left( {M \cdot \begin{bmatrix}h_{t - 1} \\x_{t}\end{bmatrix}} \right)}} & (5)\end{matrix}$

In EQUATION 5, x_(t) is the one-dimensional input vector and M is theparameter matrix used to compute the inner product with h_(t−1) andx_(t). σ( ) is the activation function, h_(t−1) is the hidden state fromthe previous time step, and h_(t) is the hidden state at the currenttime step.

FIG. 7A illustrates an example of a conventional recurrent neuralnetwork. As shown in FIG. 7A, an input vector x_(t) and the previoushidden state h_(t−1) are multiplied by the parameter matrix M to computethe current hidden state h_(t). As shown in FIG. 7A, the input vectorx_(t) may be a three-dimensional vector represented by red, blue, andgreen pixels (e.g., channels). The hidden state refers to an internalrepresentation of the model.

Furthermore, conventional recurrent neural networks may be extended toemphasize particular input dimensions of a two-dimensional input byconsidering a saliency vector a_(t):

$\begin{matrix}{h_{t} = {\sigma \left( {M \cdot \begin{bmatrix}h_{t - 1} \\{a_{t}^{T}{Xt}}\end{bmatrix}} \right)}} & (6)\end{matrix}$

In EQUATION 6, the saliency vector a_(t) is applied by a dot product onthe input matrix X_(t). The input matrix X_(t) is composed of rowvectors indicating features for different words in a sentence ordifferent locations in an image. As shown in FIG. 7B, in one example,the input matrix X_(t) is a three-dimensional matrix composed of red,blue, and green row vectors (e.g., channels). The saliency vector a_(t)may be applied to an input matrix X_(t) to emphasize particulardimensions of the input matrix X_(t). For example, the input may be asentence and specific words of the sentence may be emphasized as aresult of applying the saliency vector a_(t). In another example, theinput may be an image and specific locations of the image may beemphasized as a result of applying the saliency vector a_(t). InEQUATION 6, a_(t) ^(T) is the transpose of vector a_(t).

FIG. 7B illustrates an example of an attention recurrent neural network.

As shown in FIG. 7B, an input matrix X_(t) and a saliency vector a_(t)are combined with the previous hidden state h_(t−1) as an input to thenetwork. In one configuration, the network uses the parameter matrix Mto compute the current hidden state h_(t).

For images and/or other types of inputs with two or more dimensions,vectorizing the input into a matrix does not consider the spatialrelationship (e.g., correlation) between the elements of the input, suchas pixels. That is, the conventional attention recurrent neural networkdoes not consider the spatial structure that characterizes the inputX_(t).

Aspects of the present disclosure are directed to a neural network thatreplaces the inner products with convolutions to returnthree-dimensional output with spatial structure. The convolutionaloperation may preserve the spatial nature of an input, such as thespatial correlation of pixels in an image.

In one configuration, the three-dimensional inputs are further refinedby two-dimensional spatial attention saliency matrices that emphasizespecific (e.g., important) spatial locations in the input:

$\begin{matrix}{H_{t} = {\sigma \left( {F \star \begin{bmatrix}H_{t - 1} \\{A_{t} \odot X_{t}}\end{bmatrix}} \right)}} & (7)\end{matrix}$

In EQUATION 7, F is a multi-dimensional filter, such as amulti-dimensional convolutional filter, that processes an input, such asI_(t) of FIG. 5A, having a spatial structure. The convolutional filtermay be a spatial matrix with trainable weights to be applied to theinputs. In contrast to the hidden state h_(t) of the conventional neuralnetworks, in the present configuration, the attention recurrent neuralnetwork provides a hidden state H_(t) with a spatial structure.Furthermore, in the present configuration, the input A_(t)⊙X_(t)maintains its original spatial layout, which is weighed according to theattention matrix. Furthermore, in EQUATION 7, the filter F performs aconvolution, in contrast to an inner product performed in conventionalneural networks.

That is, the convolutional attention recurrent neural network does nottreat all input dimensions equally. Rather, as shown in EQUATION 7, thespatial dimensions are weighed according to an attention matrix thatweights each spatial dimension differently. The attention componentgenerates a feature map by weighing the spatial dimensions of an inputto the convolutional attention recurrent neural network.

FIG. 7C illustrates an example of a convolutional attention recurrentneural network according to an aspect of the present disclosure. Asshown in FIG. 7C, a three-dimensional feature map X_(t) (represented byred, blue, and green feature maps (e.g., channels)) and atwo-dimensional saliency map A_(t) (e.g., attention map) are combined byelement-wise multiplication along the spatial dimensions as input to thenetwork that uses the convolutional filter F and a previous hidden stateH_(t−1) to compute the current hidden state H_(t).

In one configuration, a class of interest for the frame I_(t) isidentified based on the multi-dimensional hidden state and trainingdata. The class of interest may be an action, an object, and/or anevent. For example, for frame 504 in FIG. 5A, the action may be jumpingand/or diving, the object may be a diver, and the event may be diving.Furthermore, training data refers to previous examples that were used totrain a neural network, such as the recurrent neural network and/or aconvolutional neural network. Using the class of interest of each frame(I₁−I_(t)) of the sequence of frames, an object and/or an action may bedetermined for the sequence of frames. For example, for frame 504 andother frames prior to and/or after frame 504 in a sequence of frames,the class of interest of each frame may be used to determine the classof interest for the sequence of frames. In this example, the class ofinterest for the sequence of frames may be diving and/or a diver.

The frame may be a two-dimensional (2D) slice of spatio-temporal data.Furthermore, the frame may be referred to as an image. In oneconfiguration, for the convolutional attention recurrent neural network,the internal representation (e.g., the hidden state) of the inputincludes a spatial layout.

The attention map provides the recurrent neural network with thelocation of action in a frame using appearance motion information, suchas optical flow. In one configuration, the optical flow may be describedby a field of vectors that indicate a predicted movement of a pixel fromone frame to the next frame. That is, the optical flow tracks action inthe pixels and considers the motion to predict the features with thegreatest saliency within the sequence of frames.

Both an attention recurrent neural network and a convolutional attentionrecurrent neural network use an attention map and a feature map as aninput. The attention map is updated by the recurrent neural network, thefeature map is updated, at each time step, when a new frame is received.In comparison to a conventional recurrent neural network, both theattention recurrent neural network and convolutional attention recurrentneural network treat the spatial dimensions of their input differently.In addition, convolutional attention recurrent neural networks improvethe use of the spatial dimensions to generate attention maps. That is,the attention recurrent neural network uses a vectorized representationof the input without considering spatial correlations In contrast, theconvolutional attention recurrent neural network uses a convolution thatconsiders the spatial correlation of the input and retains the spatialstructure over time.

According to an aspect of the present disclosure, the saliency map A_(t)is a current two-dimensional attention map. The attention map may bebased on an image (e.g., frame) of a sequence of frames (e.g., video).The attention map is generated by the convolutional attention recurrentneural network based on elements deemed important by the convolutionalattention recurrent neural network. That is, the attention map providesthe convolutional attention recurrent neural network of the location ina frame that an action takes place using appearance or motioninformation. As shown in FIGS. 5B and 7C, a new attention map A_(t+1)may be generated based on a previous hidden state H_(t) and a currentinput feature map X_(t+1).

Furthermore, as shown in FIG. 7C, the feature map X_(t) is amulti-dimensional input. As previously discussed, the multi-dimensionalfeature map X_(t) may be produced via an upper convolution layer of aconvolutional neural network (e.g., FC2 in FIG. 3B) or via a recurrentneural network, for example. The convolutional attention recurrentneural network may also include a spatial layout. The multi-dimensionalfeature map may be a 2D or 3D feature map, which may be appearance,optical flow, motion boundaries (e.g., gradient of optical flow),semantic segmentation at a feature level, and/or the like.

As an example, video processing may be specified to determine thecontent or action of a video. For example, the video processing may beused for action recognition in video, video indexing and/orcategorizing. Preserving the spatial structure for images, such asframes of a video, is desirable for video processing. That is, theconvolutional component retains the spatial layout and an attentioncomponent retains a movement layout. Thus, by using a convolutionalattention recurrent neural network, correlation between video frames ispreserved.

FIG. 8 illustrates a method 800 for processing within a convolutionalattention recurrent neural network. At block 802, the recurrent neuralnetwork generates a current multi-dimensional attention map. Theattention map indicates areas of interest (e.g., important areas) in aframe from a sequence of spatio-temporal data. In one configuration, thespatio-temporal data is a video. The attention map may be atwo-dimensional map. Furthermore, the attention map may be referred toas a saliency map. The attention map is generated by the convolutionalattention recurrent neural network based on elements deemed important bythe convolutional attention recurrent neural network.

In block 804, the recurrent neural network receives a multi-dimensionalfeature map. The multi-dimensional feature map may be athree-dimensional feature map or two-dimensional feature map.Additionally, the multi-dimensional feature map may be based on thefirst frame. Furthermore, in one configuration, after a first time step,the current multi-dimensional attention map is based on a priormulti-dimensional attention map and a second frame of the sequence ofspatio-temporal data. In one configuration, the multi-dimensionalfeature map X_(t) is produced via an upper convolution layer of aconvolutional neural network (e.g., FC2 in FIG. 3B) or via a recurrentneural network.

Additionally, in one optional configuration, at block 806, the recurrentneural network receives the multi-dimensional feature map from a layerof a convolutional neural network (CNN).

Furthermore, in block 808, the recurrent neural network convolves thecurrent multi-dimensional attention map and the multi-dimensionalfeature map to obtain a multi-dimensional hidden state and a nextmulti-dimensional attention map. That is, the current attention map andfeature map may be combined by element-wise multiplication (e.g., At⊙Xt)resulting in a weighted feature map. The recurrent neural network mayconvolve the weighted feature map and previous hidden state to obtainthe current hidden state, as shown in EQUATION 7. In one configuration,the convolving comprises applying a multi-dimensional filter.Furthermore, the convolving may be based on a prior multi-dimensionalhidden state. The next hidden state and next input feature map may beused to generate the next attention map.

In one configuration, the recurrent neural network is a convolutionalattention recurrent neural network. Furthermore, the recurrent neuralnetwork may be an attention long short-term memory (LSTM) network.

Additionally, in block 810, the recurrent neural network identifies aclass of interest in the first frame based on the multi-dimensionalhidden state and training data. As an example, video processing may bespecified to determine the content or action of a video. For example,the video processing may be used for action recognition in video, videoindexing and/or categorizing. In one configuration, the class ofinterest is an action, an object, and/or an event

Further optionally, at block 812, the recurrent neural networkdetermines a class of interest in the sequence of spatio-temporal databased on the class of interest in the first frame and a class ofinterest of at least a second frame from the sequence of spatio-temporaldata.

In some aspects, method 800 may be performed by the SOC 100 (FIG. 1) orthe system 200 (FIG. 2). That is, each of the elements of method 800may, for example, but without limitation, be performed by the SOC 100 orthe system 200 or one or more processors (e.g., CPU 102 and localprocessing unit 202) and/or other components included therein.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to, a circuit, anapplication specific integrated circuit (ASIC), or processor. Generally,where there are operations illustrated in the figures, those operationsmay have corresponding counterpart means-plus-function components withsimilar numbering.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Additionally, “determining” may include receiving (e.g., receivinginformation), accessing (e.g., accessing data in a memory) and the like.Furthermore, “determining” may include resolving, selecting, choosing,establishing and the like.

As used herein, a phrase referring to “at least one of a list of” itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general-purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general-purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory,erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, a hard disk, aremovable disk, a CD-ROM and so forth. A software module may comprise asingle instruction, or many instructions, and may be distributed overseveral different code segments, among different programs, and acrossmultiple storage media. A storage medium may be coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium may beintegral to the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware, or any combination thereof. If implemented in hardware, anexample hardware configuration may comprise a processing system in adevice. The processing system may be implemented with a busarchitecture. The bus may include any number of interconnecting busesand bridges depending on the specific application of the processingsystem and the overall design constraints. The bus may link togethervarious circuits including a processor, machine-readable media, and abus interface. The bus interface may be used to connect a networkadapter, among other things, to the processing system via the bus. Thenetwork adapter may be used to implement signal processing functions.For certain aspects, a user interface (e.g., keypad, display, mouse,joystick, etc.) may also be connected to the bus. The bus may also linkvarious other circuits such as timing sources, peripherals, voltageregulators, power management circuits, and the like, which are wellknown in the art, and therefore, will not be described any further.

The processor may be responsible for managing the bus and generalprocessing, including the execution of software stored on themachine-readable media. The processor may be implemented with one ormore general-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Software shall be construed broadly to meaninstructions, data, or any combination thereof, whether referred to assoftware, firmware, middleware, microcode, hardware descriptionlanguage, or otherwise. Machine-readable media may include, by way ofexample, random access memory (RAM), flash memory, read only memory(ROM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable programmable Read-onlymemory (EEPROM), registers, magnetic disks, optical disks, hard drives,or any other suitable storage medium, or any combination thereof. Themachine-readable media may be embodied in a computer-program product.The computer-program product may comprise packaging materials.

In a hardware implementation, the machine-readable media may be part ofthe processing system separate from the processor. However, as thoseskilled in the art will readily appreciate, the machine-readable media,or any portion thereof, may be external to the processing system. By wayof example, the machine-readable media may include a transmission line,a carrier wave modulated by data, and/or a computer product separatefrom the device, all which may be accessed by the processor through thebus interface. Alternatively, or in addition, the machine-readablemedia, or any portion thereof, may be integrated into the processor,such as the case may be with cache and/or general register files.Although the various components discussed may be described as having aspecific location, such as a local component, they may also beconfigured in various ways, such as certain components being configuredas part of a distributed computing system.

The processing system may be configured as a general-purpose processingsystem with one or more microprocessors providing the processorfunctionality and external memory providing at least a portion of themachine-readable media, all linked together with other supportingcircuitry through an external bus architecture. Alternatively, theprocessing system may comprise one or more neuromorphic processors forimplementing the neuron models and models of neural systems describedherein. As another alternative, the processing system may be implementedwith an application specific integrated circuit (ASIC) with theprocessor, the bus interface, the user interface, supporting circuitry,and at least a portion of the machine-readable media integrated into asingle chip, or with one or more field programmable gate arrays (FPGAs),programmable logic devices (PLDs), controllers, state machines, gatedlogic, discrete hardware components, or any other suitable circuitry, orany combination of circuits that can perform the various functionalitydescribed throughout this disclosure. Those skilled in the art willrecognize how best to implement the described functionality for theprocessing system depending on the particular application and theoverall design constraints imposed on the overall system.

The machine-readable media may comprise a number of software modules.The software modules include instructions that, when executed by theprocessor, cause the processing system to perform various functions. Thesoftware modules may include a transmission module and a receivingmodule. Each software module may reside in a single storage device or bedistributed across multiple storage devices. By way of example, asoftware module may be loaded into RAM from a hard drive when atriggering event occurs. During execution of the software module, theprocessor may load some of the instructions into cache to increaseaccess speed. One or more cache lines may then be loaded into a generalregister file for execution by the processor. When referring to thefunctionality of a software module below, it will be understood thatsuch functionality is implemented by the processor when executinginstructions from that software module. Furthermore, it should beappreciated that aspects of the present disclosure result inimprovements to the functioning of the processor, computer, machine, orother system implementing such aspects.

If implemented in software, the functions may be stored or transmittedover as one or more instructions or code on a computer-readable medium.Computer-readable media include both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage medium may be anyavailable medium that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Additionally, anyconnection is properly termed a computer-readable medium. For example,if the software is transmitted from a website, server, or other remotesource using a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects computer-readable media maycomprise non-transitory computer-readable media (e.g., tangible media).In addition, for other aspects computer-readable media may comprisetransitory computer-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of computer-readablemedia.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer-readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

What is claimed is:
 1. A method of processing data within aconvolutional attention recurrent neural network (RNN), comprising:generating a current multi-dimensional attention map, the currentmulti-dimensional attention map indicating areas of interest in a firstframe from a sequence of spatio-temporal data; receiving amulti-dimensional feature map; convolving the current multi-dimensionalattention map and the multi-dimensional feature map to obtain amulti-dimensional hidden state and a next multi-dimensional attentionmap; and identifying a class of interest in the first frame based on themulti-dimensional hidden state and training data.
 2. The method of claim1, further comprising determining a class of interest in the sequence ofspatio-temporal data based on the class of interest in the first frameand a class of interest of at least a second frame from the sequence ofspatio-temporal data.
 3. The method of claim 1, in which the class ofinterest is at least one of an action, an object, an event, or acombination thereof.
 4. The method of claim 1, in which the convolvingcomprises applying a multi-dimensional filter.
 5. The method of claim 4,in which the convolving is further based on a prior multi-dimensionalhidden state.
 6. The method of claim 1, in which the convolutionalattention RNN comprises an attention long short-term memory (LSTM)network.
 7. The method of claim 1, in which the multi-dimensionalfeature map is based on the first frame.
 8. The method of claim 1, inwhich, after a first time step, the current multi-dimensional attentionmap is based on a prior multi-dimensional attention map and a secondframe of the sequence of spatio-temporal data.
 9. The method claim 1,further comprising receiving the multi-dimensional feature map from alayer of a convolutional neural network (CNN).
 10. The method of claim1, in which the spatio-temporal data is a video.
 11. An apparatus forprocessing data within a convolutional attention recurrent neuralnetwork (RNN), comprising: a memory; and at least one processor coupledto the memory, the at least one processor configured: to generate acurrent multi-dimensional attention map, the current multi-dimensionalattention map indicating areas of interest in a first frame from asequence of spatio-temporal data; to receive a multi-dimensional featuremap; to convolve the current multi-dimensional attention map and themulti-dimensional feature map to obtain a multi-dimensional hidden stateand a next multi-dimensional attention map; and to identify a class ofinterest in the first frame based on the multi-dimensional hidden stateand training data.
 12. The apparatus of claim 11, in which the at leastone processor is further configured to determine a class of interest inthe sequence of spatio-temporal data based on the class of interest inthe first frame and a class of interest of at least a second frame fromthe sequence of spatio-temporal data.
 13. The apparatus of claim 11, inwhich the class of interest is at least one of an action, an object, anevent, or a combination thereof.
 14. The apparatus of claim 11, in whichthe at least one processor is further configured to apply amulti-dimensional filter to convolve the current multi-dimensionalattention map and the multi-dimensional feature map.
 15. The apparatusof claim 14, in which the at least one processor is further configuredto convolve based on a prior multi-dimensional hidden state.
 16. Theapparatus of claim 11, in which the convolutional attention RNNcomprises an attention long short-term memory (LSTM) network.
 17. Theapparatus of claim 11, in which the multi-dimensional feature map isbased on the first frame.
 18. The apparatus of claim 11, in which, aftera first time step, the current multi-dimensional attention map is basedon a prior multi-dimensional attention map and a second frame of thesequence of spatio-temporal data.
 19. The apparatus claim 11, in whichthe at least one processor is further configured to receive themulti-dimensional feature map from a layer of a convolutional neuralnetwork (CNN).
 20. The apparatus of claim 11, in which thespatio-temporal data is a video.
 21. A non-transitory computer-readablemedium having program code recorded thereon for processing data within aconvolutional attention recurrent neural network (RNN), the program codebeing executed by a processor and comprising: program code to generate acurrent multi-dimensional attention map, the current multi-dimensionalattention map indicating areas of interest in a first frame from asequence of spatio-temporal data; program code to receive amulti-dimensional feature map; program code to convolve the currentmulti-dimensional attention map and the multi-dimensional feature map toobtain a multi-dimensional hidden state and a next multi-dimensionalattention map; and program code to identify a class of interest in thefirst frame based on the multi-dimensional hidden state and trainingdata.
 22. The non-transitory computer-readable medium of claim 21, inwhich program code further comprises program code to determine a classof interest in the sequence of spatio-temporal data based on the classof interest in the first frame and a class of interest of at least asecond frame from the sequence of spatio-temporal data.
 23. Thenon-transitory computer-readable medium of claim 21, in which the classof interest is at least one of an action, an object, an event, or acombination thereof.
 24. The non-transitory computer-readable medium ofclaim 21, in which the program code further comprises program code toapply a multi-dimensional filter to convolve the currentmulti-dimensional attention map and the multi-dimensional feature map.25. The non-transitory computer-readable medium of claim 24, in whichthe program code to apply further comprises program code to convolvebased on a prior multi-dimensional hidden state.
 26. An apparatus forprocessing data within a convolutional attention recurrent neuralnetwork (RNN), comprising: means for generating a currentmulti-dimensional attention map, the current multi-dimensional attentionmap indicating areas of interest in a first frame from a sequence ofspatio-temporal data; means for receiving a multi-dimensional featuremap; means for convolving the current multi-dimensional attention mapand the multi-dimensional feature map to obtain a multi-dimensionalhidden state and a next multi-dimensional attention map; and means foridentifying a class of interest in the first frame based on themulti-dimensional hidden state and training data.
 27. The apparatus ofclaim 26, further comprising means for determining a class of interestin the sequence of spatio-temporal data based on the class of interestin the first frame and a class of interest of at least a second framefrom the sequence of spatio-temporal data.
 28. The apparatus of claim26, in which the class of interest is at least one of an action, anobject, an event, or a combination thereof.
 29. The apparatus of claim26, in which the means for convolving further comprises means forapplying a multi-dimensional filter.
 30. The apparatus of claim 29, inwhich the means for convolving is further based on a priormulti-dimensional hidden state.